Short summary of how is the lined paper important: you can think about bias as some sort of perturbation. You are then interested in the “cascade of spreading” of the perturbation, and especially factors like the distribution of sizes of cascades. The universality classes tell you this can be predicted by just a few parameters (Table 1 in the linked paper) depending mainly on local dynamic (forecaster-forecaster interactions). Now if you have a good model of the local dynamic, you can determine the parameters and determine into which universality class the problem belongs. Also you can try to infer the dynamics if you have good data on your interactions.
I’m afraid I don’t know enough about how “forecasting communities” work to be able to give you some good guesses what may be the points of leverage. One quick idea, if you have everybody on the same platform, may be to do some sort fo A/B experiment—manipulate the data so some forecasters would see the predictions of other with an artificially introduced perturbation, and see how their output will be different from the control group. If you have data on “individual dynamics” liken that, and some knowledge of network structure, the theory can help you predict the cascade size distribution.
(I also apologize for not being more helpful, but I really don’t have time to work on this for you.)
Short summary of how is the lined paper important: you can think about bias as some sort of perturbation. You are then interested in the “cascade of spreading” of the perturbation, and especially factors like the distribution of sizes of cascades. The universality classes tell you this can be predicted by just a few parameters (Table 1 in the linked paper) depending mainly on local dynamic (forecaster-forecaster interactions). Now if you have a good model of the local dynamic, you can determine the parameters and determine into which universality class the problem belongs. Also you can try to infer the dynamics if you have good data on your interactions.
I’m afraid I don’t know enough about how “forecasting communities” work to be able to give you some good guesses what may be the points of leverage. One quick idea, if you have everybody on the same platform, may be to do some sort fo A/B experiment—manipulate the data so some forecasters would see the predictions of other with an artificially introduced perturbation, and see how their output will be different from the control group. If you have data on “individual dynamics” liken that, and some knowledge of network structure, the theory can help you predict the cascade size distribution.
(I also apologize for not being more helpful, but I really don’t have time to work on this for you.)